While there’s still some time until the 2015 AAG Annual Meeting kicks off in Chicago next spring the deadline for submitting papers is approachingalmost here: November 20th, 2014!

As for me, I will present an algorithm I developed as part of my PhD thesis and in the course of my related research of people’s movements in urban areas:

Konstantin Greger, University of TsukubaA Spatio-Temporal Betweenness Centrality Measure for the Micro-Scale Estimation of Pedestrian Traffic

The spatio-temporal mobile population estimation approach I introduce here can be used to calculate an index for the pedestrian traffic volume on street segments divided into deliberately chosen time steps. This is especially useful in the spatial context of highly urbanized areas, as it provides the populations in public space as a complementary element to building populations.

This was achieved by employing a graph theory methodology, namely that of betweenness centrality, and extending it by the temporal dimension. This new model was then applied using a number of datasets that provide information about building populations and train station passenger transfers segregated both spatially and by time.

The introduction of the temporal dimension to the estimation of populations in public space allows for a micro-scale analysis of the actual population figures according to the underlying human activities. I believe that this is the most interesting characteristic of the proposed estimation methodology, since for the first time it allows for a reliable estimation of mobile populations even for large study areas with justifiable requirements in terms of both necessary input data and computational expense.

The output result of the spatio-temporal model can be used to visualize the amount of pedestrians on the streets of a chosen study area. While the data do not represent the absolute numbers of pedestrians, they do reflect the traffic volume and allow for a comparison of crowdedness, which can be used for further quantitative analyses, such as population density calculations for certain points in time.

This year I made an effort to not being placed into some random session as has happened to me both in 2012 and 2014 – in 2013 I went all the way and organized my very own session. Therefore I browsed the (admittedly a wee bit confusing) “abstract and session submission console” on the AAG conference website. There I came across an effort by Prof. Diansheng Guo at the University of South Carolina, who proposed a session (or a series thereof?) labeled “Spatial Data Mining and Big Data Analytics”. I was more than happy to receive an almost instantaneous feedback from Prof. Guo, let alone a positive one!

Obviously I don’t have details about the “where and when”s of said session(s) and my presentation, but I will update this article accordingly once the information has become available. The details are:

It’s been awfully quiet here on the blog recently. This is owed to some major changes in my life, including the successful end of my PhD program, a successful job hunt, a move from Japan to Germany, and an interesting yet challenging start in my new job at a major German research institute.

But the recent release of MacOS 10.10 “Yosemite” together with the even more recent release of the new QGIS 2.6 “Brighton” was a brilliant opportunity to not only bring back some life here, but also to continue my mini-series of articles about installing and running QGIS and other rather scientific software packages on the latest versions of MacOS (see here, here, and here for example).

So I sat down on my freshly delivered sofa between unpacked boxes to try my luck. To make a long story short, in my case the installation ran smoothly and was done in about half an hour – downloading the necessary disk images took most of time. But before I updated my QGIS 2.4 to the new version 2.6 I first tried if 2.4 still runs on my freshly upgraded MacOS Yosemite. And there was a small surprise waiting for me here, as MacOS asked me to update my Java SE 6 runtime!

Updated Java SE 6 runtime necessary

Luckily this was no big deal, since the error message provided a link to the download page at Apple.

Apple provides the Java update

Easy installation of the Java update

After running this update QGIS 2.4 worked fine like before.

For the download of QGIS itself I decided once again for the packages provided by William Kyngesburye a.k.a KyngChaos – not only did I never have any problems with these, but to my best knowledge they are the only available pre-compiled QGIS packages for MacOS… The installation process follows the steps known from earlier releases:

1. GDAL
First is the new GDAL 1.11. The installation is as easy as downloading the DMG and installing GDAL from the respective PKG therein. Please ignore the NumPy package also contained in the GDAL disk image, since it’s an outdated version. Oh ya, and then there’s this thing that’s still annoying me:

That’s still annoying

Gatekeeper refuses to open applications and packages from “unidentified developers” (that is, developers that can’t afford a certificate by Apple) by double-clicking. Hence you need to right-click it and select Open.

2. matplotlib and NumPy
Before we can install matplotlib we need to install NumPy. There you can find the most recent version 1.8.0-1. As is stated on the website NumPy is “included on the GDAL Framework disk image, though it may not be up to date”. And indeed the GDAL image mentioned above includes NumPy 1.6.2-1 from mid-2012…
Now that that’s out of the way we can install matplotlib 1.3.1-2.

3. QGIS
And finally QGIS 2.6.0-1 itself. As in the other cases we open the DMG file and install from the PKG file therein. That’s it!

QGIS 2.6 “Brighton” splash screen

QGIS 2.6 “Brighton” UI on Yosemite

Now that everything was installed it was time to fire it up for the first time. And lo and behold, it works! Just like that. You can’t ask for more. Now it’s time to discover all the great new features QGIS 2.6 brings!